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@JonnyWong16
JonnyWong16 / hide_episode_spoilers.py
Last active August 2, 2026 20:45
Automatically change episode artwork in Plex to hide spoilers.
Updated script link:
https://github.com/blacktwin/JBOPS/blob/master/utility/hide_episode_spoilers.py
@arianvp
arianvp / SSH_MACOS_SECURE_ENCLAVES.md
Last active August 2, 2026 20:43
Native Secure Enclaved backed ssh keys on MacOS

Native Secure Enclave backed ssh keys on MacOS

It turns out that MacOS Tahoe can generate and use secure-enclave backed SSH keys! This replaces projects like https://github.com/maxgoedjen/secretive

There is a shared library /usr/lib/ssh-keychain.dylib that traditionally has been used to add smartcard support to ssh by implementing PKCS11Provider interface. However since recently it also implements SecurityKeyProivder which supports loading keys directly from the secure enclave! SecurityKeyProvider is what is normally used to talk to FIDO2 devices (e.g. libfido2 can be used to talk to your Yubikey). However you can now use it to talk to your Secure Enclave instead!

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

@warrior-graph
warrior-graph / pi-setup.md
Last active August 2, 2026 20:39
My Pi Setup

Resumo: configurar o Pi coding agent

Passo a passo rápido pra instalar o Pi agent com DeepSeek, RTK e extensões.


1. Instalar o Pi agent

npm install -g --ignore-scripts @earendil-works/pi-coding-agent
@AveYo
AveYo / . Pitch Black Theme.reg
Last active August 2, 2026 20:35
Pitch Black Theme.reg - now for Ctrl+Alt+Del (and logon on 11) as well - revised 2026-05-15
Windows Registry Editor Version 5.00
; Pitch Black Theme with Gray Accent preset by AveYo
; for Ctrl+Alt+Del, Logon, Taskbar, Start Menu, Action Center (10 & 11)
; revised 2026.05.15
[HKEY_CURRENT_USER\SOFTWARE\Microsoft\Windows\CurrentVersion\Explorer\Accent]
"AccentColorMenu"=dword:ff000000 ; Window borders and titlebar [black]
"StartColorMenu"=dword:ff000000 ; Modals in UWP ex. Apply new refresh rate in 10 [black]
"AccentPalette"=hex:\
@ogerardin
ogerardin / gist:014aff71b7d7aacf4bbfb3b8bbc575b7
Created July 25, 2023 14:18
Convert VMware VM to UTM (Mac)
Export VM as OVF
/Applications/VMware\ Fusion.app/Contents/Library/VMware\ OVF\ Tool/ovftool VM.vmwarevm/VM.vmx .
Convert .vmdk to .qcow2
qemu-img convert -p -f vmdk -O qcow2 VM-disk1.vmdk VM-disk1.qcow2
Create custom VM in UTM matching VM hardware
Remove default disk
Add drive / import QCOW2 file (file is copied to default UTM VM folder)
Add CD/DVD drive
@Klerith
Klerith / instalaciones.md
Last active August 2, 2026 20:23
Instalaciones recomendadas - Curso de Angular de cero a experto
@rahulkhatri137
rahulkhatri137 / cocMapping.json
Created July 14, 2026 18:27 — forked from pghant/cocMapping.json
Clash of Clans JSON Export Mapping
[
{"name":"Action Figure","dataId":90000048},
{"name":"Air Bomb","dataId":12000005},
{"name":"Air Bombs","dataId":1000054},
{"name":"Air Defense","dataId":1000012},
{"name":"Air Sweeper","dataId":1000028},
{"name":"Alchemist","dataId":null},
{"name":"Angry Jelly","dataId":73000011},
{"name":"Apprentice Warden","dataId":4000097},
{"name":"Archer Puppet","dataId":90000002},
@lokafinnsw
lokafinnsw / README.md
Created December 26, 2025 04:04
NeMo ASR + Speaker Diarization Setup (Parakeet-TDT v3, ~80x realtime). Includes Dockerfile, YAML config, and Python example with word-level timestamps for subtitle generation.

NeMo Fast Diarization Setup

Production-ready ASR + Speaker Diarization using NVIDIA NeMo.

Why is this fast?

  1. Model Architecture: Parakeet-TDT uses FastConformer (not Whisper's attention-heavy encoder)
  2. Native Pipelines: The YAML config triggers optimized C++/CUDA code for VAD and clustering
  3. TitaNet Embeddings: Much faster than x-vector or ECAPA-TDNN for speaker separation
  4. BF16 Inference: ~30% VRAM savings on Ada Lovelace GPUs (RTX 4000/4090)